Insegnamento a.a. 2019-2020


Department of Decision Sciences

Course taught in English

Supported by Siemens

Go to class group/s: 31
CLEAM (6 credits - II sem. - OP  |  SECS-S/06) - CLEF (6 credits - II sem. - OP  |  SECS-S/06) - CLEACC (6 credits - II sem. - OP  |  SECS-S/06) - BESS-CLES (6 credits - II sem. - OP  |  SECS-S/06) - WBB (6 credits - II sem. - OP  |  SECS-S/06) - BIEF (6 credits - II sem. - OP  |  SECS-S/06) - BIEM (6 credits - II sem. - OP  |  SECS-S/06) - BIG (6 credits - II sem. - OP  |  SECS-S/06) - BEMACS (6 credits - II sem. - OP  |  SECS-S/06)
Course Director:

Classes: 31 (II sem.)

Suggested background knowledge

It is suggested for students to have attended a basic course on mathematics and a basic course on statistics before this course.

Mission & Content Summary


The scope of this course is to offer participants a thorough exploration of business analytics and of how computational modelling can be combined with big data to achieve given industry goals. In a well known communication to the European Parliament on 2 July 2014, the European Community evidenced the need of training a generation of managers who know how to combine information derived from data into models to support decisions. In fact, in recent years, the data driven revolution is changing the way in which institutions and corporations make decisions. We are heading towards industry 4.0. The great availability of data, the increased computing and information technology capabilities are creating new jobs and changing the way in which companies operate. The February 2018 report of the UK Government Office for Science highlights that computational modelling is a source of competitive advantage for corporations. In the first part of the course, participants are exposed to the fundamental theoretical and methodological basis, analyzing relevant quantitative and mathematical methods. In the second part, students are exposed to industry case studies. With the participation of data scientists and experts coming from the industry participants discover how innovative methods based on big data and information technology have allowed to solve modern industrial problems.


  • The Principles of Machine Learning.
  • Formulation of quantitative models via Linear Programs.
  • The symplex method, Duality.
  • Sensitivity Analysis.
  • Network Type Problems.
  • Big Data and Lasso: The Dantzig Selector.
  • Industry 4.0 and Descriptive Analytics: Business Case studies.

These case studies are discussed and solved in the presence of guest lecturers from Siemens and from the involved partner companies.

Intended Learning Outcomes (ILO)


At the end of the course student will be able to...
  • Formulate a computational model to solve business and management problems.
  • Appreciate the principles and the solution algorithms of linear programs at the basis of dedicated software for their application.
  • Distinguish the wide range of business problems whose solution is supported by computational models.
  • Recognize the challenges posed to quantitative methods by large dimensionality and big data and identify the corresponding technological solutions.


At the end of the course student will be able to...
  • Organize information to build a quantitative model in line with the input posed.
  • Translate a business problem into a corresponding computational modelling frame.
  • Use dedicated software in order to obtain quantitative information.
  • Interpret solutions derived from implementing the chosen model in order to make optimal decisions.
  • Analyze quantitative models with sensitivity analysis tools to obtain "managerial insights".

Teaching methods

  • Face-to-face lectures
  • Guest speaker's talks (in class or in distance)
  • Case studies /Incidents (traditional, online)


The course makes use of a combination of teaching techniques. Face-to-face lectures are used for the sessions in which methodological and theoretical parts of the paper are proposed and discussed.

  • In these sessions students are assisted in identifying the quantitative model, in implementing the model through dedicated software and in performing sensitivity analysis.
  • In the second part of the course, students are exposed to the solution of industry case studies presented in a triplet of lectures. After the exposition by the experts of the industrial problem, participants are introduced to the methods of solution and are guided in critically discussing the results, the methodologies adopted and in identifying weaknesses and remaining open questions.

Assessment methods

  Continuous assessment Partial exams General exam
  • Written individual exam (traditional/online)
  x x


Assessment, is performed as follows.

  • For the first part of the course students solve mathematical problems divided into open-ended numerical questions and multiple-choice questions.
  • For the second part of the course there are multiple choice questions concerning the methodological aspects of the illustrated case studies and the material developed in the second part of the course.

Students can chose to take either a final general written exam or the combination of a partial exam and a work group.

The partial exam will cover the first part of the course, with a maximum score of 21 points. The work group will have a maximum score of 10 points and will be about the solution of a business case related to the material introduced in the second part of the course.

The general written exam will cover the entire course material, with a prevalence of problems written in a mathematical form, and will correspond to a total of 31 points.

Teaching materials


  • R.J. VANDERBEI,  Linear Programming, Springer Series in Operational Research and Management Science, 2014, Fourth Edition, ISBN 978-1-4614-7629-0.
  • F.S. HILLIER and G.J. LIEBERMAN, Introduction to Operations Research, Second Edition, 2001.
  • Teaching notes and slides provided by the teachers.
Last change 01/06/2019 17:59